Advanced Microarray Data Analysis

Advanced Microarray Data Analysis will be held on the JCVI campus. This course will cover advanced data analysis techniques that are appropriate for the two-dye spotted microarray platform. The content is designed for those with experience in microarray data analysis; prior participation in either the PFGRC's Introduction to Microarray Data Analysis or Introduction to Microarray Technology course (or their equivalent) is required. Lectures, hands-on bioinformatics sessions, and group discussions will be combined to effectively cover a wide range of topics.

Course topics include the characterization of microarray data, gene expression normalization strategies, CGH-specific analysis considerations and approaches, advanced data mining algorithms and statistical techniques. The course will conclude with a data analysis workshop session where students will apply a variety of advanced analysis strategies while working with a published microarray dataset. The open-source TM4 software suite will be used extensively throughout the course; CDs containing the TM4 suite as well as all course materials and datasets will be distributed.

The course will be provided free of charge. Attendees will be responsible for their travel and lodging. This advanced course is one of several offered by the PFGRC on a variety of functional genomics topics. Attendance will be limited to 16 participants.

Upcoming Dates for the Course

January 13-14, 2009

Location

J. Craig Venter Institute
9704 Medical Center Drive, Rockville, MD 20850 (directions)

Course Topics

Characterization of microarray data

Provides descriptions of the nature of microarray data, the characterization of microarray data distributions using descriptive statistics and the interpretation of diagnostic plots and what they can reveal

Gene expression normalization strategies

Presents theoretical and practical aspects of commonly applied normalization techniques

CGH-specific analysis considerations and approaches

Includes a brief overview of CGH technology, explanations of the key challenges encountered when handling CGH data, and demonstrations focusing on current and novel approaches to the normalization of CGH data

Advanced data mining algorithms

Introduces tree resampling methods, dimensional reduction, neural network clustering techniques and classification algorithms

Statistical concepts and techniques

Covers concepts relevant to statistical tests applied to microarray data and introduces parametric and nonparametric techniques applicable to several experimental designs

Research data workshop and discussion

Practical data tutorial and workshop that illustrates key analysis concepts and provides tips using a published dataset as a source

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